How does the human fungal pathogen, Candida albicans, exploit its metabolic flexibility to enhance its virulence?

Project offered for Ker Memorial PhD Studentship in Infectious Diseases

Fungal pathogens kill over a million people every year. The most common human fungal pathogen is Candida albicans, a WHO-priority target. With only 3 classes of antifungal drugs available and increasing drug-resistant infections in clinical settings, understanding the mechanisms of resistance is a priority. Candida’s cellular survival in the complex and dynamic host environment depends on the ability to efficiently control its metabolism, which involves the production and breakdown of numerous different small biological chemicals, including sugars, amino acids and other molecules, collectively called "metabolites". Candida is known to assimilate glucose and alternative carbon sources simultaneously, thereby providing growth advantages, and has developed strategies for maintaining the balance between essential and toxic levels of metabolites. However, how this remarkable metabolic flexibility is regulated during infection, remains largely unknown.

The Ubiquitin-Proteasome System, UPS for short, is known to be responsible for eliminating unwanted, superfluous or toxic proteins that would otherwise damage Candida cells. Molecular machines, called E3 ubiquitin ligases, ensure that the UPS destroys only those proteins whose functions should be terminated, and spares the majority of proteins, required for ongoing cellular functions. One of the first UPS-dependent mechanism identified in metabolic regulation is mediated by the budding yeast GID E3 ubiquitin ligase complex, which targets unnecessary metabolic enzymes for proteasomal degradation upon changes in carbon sources. Evidence from the Makrantoni lab suggests that Candida employs the GID E3 complex during host infection to rewire metabolic pathways under dramatic changes of metabolite resources. This project aims to:

(1) Uncover the molecular mechanism by which GID E3 ligase regulates metabolic flexibility in Candida.

• Identify and characterize GID-dependent ubiquitylation substrate(s) that are targeted by the UPS using combined approaches of genetics, proteomics and biochemistry/crystallography.
• Investigate how GID and GID-dependent substrates regulate metabolite flux using metabolomics and live-cell imaging (microfluidics).

(2) Test how GID-mediated regulation of key metabolites affects host-adaptation and antifungal drug-resistance using in-vitro host-pathogen infection model systems for screening multiple drugs.

• Determine the impact of GID and GID-dependent substrates in Candida-macrophage interactions.
• Assess the influence of GID and GID-dependent substrates in Candida drug resistance during in vitro and in vivo infection models.

In this interdisciplinary PhD project, we will use state-of-the art methods and employ an international collaboration between the IRR, Edinburgh and the Max Planck Institute of Biochemistry, Munich to understand how metabolic flexibility in Candida contributes to antifungal drug resistance and increased virulence. The results of this research will be a springboard for future discovery of new drug targets for antifungal therapies, an urgent need for medical research.

1. Makrantoni lab
2. Anderson lab
3. Schulman lab



Training and skills development

This is an interdisciplinary PhD project at the interface between genetics, structural biology/biochemistry, ‘omics’-based approaches (proteomics, metabolomics) and host-pathogen infection model systems.

Skills that will be developed include:

1. Sophisticated CRISPR-mediated genome engineering and Molecular Genetics in Candida albicans 
2. Live-cell-imaging (microfluidics)
3. Fundamental biochemistry and structural biology skills, including recombinant protein expression and purification, X-ray crystallography and Cryo-Electron microscopy.  
4. Unbiased proteomics and metabolomics studies
5. Host-pathogen infection model systems, including in vitro and in vivo macrophage‑centric approaches
6. Advanced analysis of large, complex data sets using Python and training/applying machine-learning based models. They will gain more specialised quantitative skills in structural modelling and simulation of protein assemblies, as well as computational analysis of their own crystallographic and electron microscopy data.

Students will benefit from supervision by a multi-disciplinary team with complementary expertise, and a friendly and collaborative environment. Will be able to train hands-on in two different research environments (Edinburgh and Munich). Finally, they will benefit from a rich programme of transferable academic skills training provided by the University of Edinburgh, including leadership and communication training. Most importantly, at the end of this project the student will have all of the necessary skills to seamlessly transition between biological, clinical, and computational elements of biomedical science for their next career stage in their desired field (including non-academic career options).